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rpf (version 0.36)

Response Probability Functions

Description

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions are made available with R_RegisterCCallable.

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Version

Install

install.packages('rpf')

Monthly Downloads

20,661

Version

0.36

License

GPL (>= 3)

Maintainer

Joshua Pritikin

Last Published

July 26th, 2014

Functions in rpf (0.36)

Compute the S fit statistic for a set of items

The base class for 1 dimensional response probability functions.

ChenThissen1997

Computes local dependence indices for all pairs of items

Liking for Science dataset

compressDataFrame

Compress a data frame into unique rows and frequencies

Map an item model, item parameters, and person trait score into a information vector

Compute the S fit statistic for 1 item

Retrieve a description of the given parameter

Length of the item model vector

Write a flexMIRT PRM file

Knox Cube Test dataset

Generates item parameters

Rescale item parameters

Transform from [0,1] to the reals

itemOutcomeBySumScore

Produce an item outcome by observed sum-score table

Item parameter derivatives

Find the point where an item provides mean maximum information

Compute the ordinal gamma association statistic

orderCompletely

Order a data.frame by missingness and all columns

Read a flexMIRT PRM file

The base class for multi-dimensional response probability functions.

Class rpf.1dim.grm

The unidimensional graded response item model.

Map an item model, item parameters, and person trait score into a probability vector

Class rpf.mdim.nrm

The nominal response item model (both unidimensional and multidimensional models have the same parameterization).

Find the point where an item provides mean maximum information

Class rpf.mdim.mcm

The multiple-choice response item model (both unidimensional and multidimensional models have the same parameterization).

Class rpf.mdim.grm

The multidimensional graded response item model.

Create a nominal response model

expandDataFrame

Expand summary table of patterns and frequencies

Create a dichotomous response model

Class rpf.mdim.drm

Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).

Randomly sample response patterns given a list of items

Compute EAP scores

rpf.1dim.residual

Calculate residuals

sumScoreEAPTest

Conduct the sum-score EAP distribution test

Convert an OpenMx MxModel object into an IFA group

Item derivatives with respect to the location in the latent space

Class rpf.1dim.graded

The base class for 1 dimensional graded response probability functions.

An introduction

rpf - Response Probability Functions

Map an item model, item parameters, and person trait score into a probability vector

Create a similar item specification with the given number of factors

Convert an rpf item model name to an ID

Class rpf.mdim.graded

The base class for multi-dimensional graded response probability functions.

Multinomial fit test

Class rpf.1dim.drm

Unidimensional dichotomous item models (1PL, 2PL, and 3PL).

The base class for response probability functions.

rpf.1dim.stdresidual

Calculate standardized residuals

Compute the sum-score EAP table

Monte-Carlo test for cross-tabulation tables

ptw2011.gof.test

Compute the P value that the observed and expected tables come from the same distribution

The ogive constant

Calculate item and person Rasch fit statistics

omitMostMissing

Omit items with the most missing data

rpf.1dim.moment

Calculate cell central moments

Create a multiple-choice response model

Tabulate data.frame rows

observedSumScore

Compute the observed sum-score

Create a graded response model

Length of the item parameter vector